Anees Amjad

AI engineering · Data products · Research

Anees Amjad

I build AI tools and data products for real-world decisions.

Built work across internal AI, public-sector software, and research. MS Computational Analysis and Public Policy, University of Chicago.

Explore selected workGet in touch

01Built and deployed internally

Ourea Brain

Company knowledge, accessible through Slack.

Explanatory diagram · simplified architectureInteractive architecture walkthrough · select a stage

My implementation
  1. Question asked in Slack
  2. App on Google Cloud Run
  3. Company context + Claude API
  4. Response in Slack

Overview · all four stages

A colleague’s question travels from Slack to the app, which brings in company context and Anthropic’s Claude API, and the answer returns to Slack. This walkthrough explains the system; it is not a live AI demo.

  1. Stage 01 · Question asked in Slack

    A colleague at Ourea Energy asks a question in Slack, where the team already works.

    My part: I built the app’s connection to Slack.

  2. Stage 02 · App on Google Cloud Run

    The Slack-connected app runs on Google Cloud Run and handles the question.

    My part: I built and deployed this app.

  3. Stage 03 · Company context + Claude API

    The app draws on company context from connected sources — Slack, Gmail, Google Docs, and meeting notes — and uses Anthropic’s Claude API to form an answer.

    My part: I built these source connections and the use of the Claude API.

  4. Stage 04 · Response in Slack

    The answer is returned in Slack. In an early live deployment, it answered a Slack question using meeting-note context.

Simplified architecture; highlighted stages show my implementation.
What it does
An internal AI assistant that brings company context into Slack for colleagues at Ourea Energy, designed to help them retrieve project context, identify owners, and follow up on decisions.
What I built
As an Agentic AI Intern, I built and deployed it: the Slack-connected app on Google Cloud Run, its connections to Slack, Gmail, Google Docs, and meeting notes, and its use of Anthropic’s Claude API.
Observed behaviour
An early live deployment answered a Slack question using meeting-note context.Internal tool.

ToolsSlack integrationAnthropic Claude APIGoogle Cloud RunGoogle Workspace sourcesmeeting notesSecret Manager

02Co-founder, Engineering and Data

PublicPath

Making public-sector opportunities easier to find.

PublicPath homepage with the headline ‘Your path into public service starts here’.
PublicPath team homepage, captured 6 October 2026. My contribution focused on ingestion and data quality. Project website ↗Job search was not loading when checked on 6 Oct 2026.

Explanatory diagram · data flow

  1. External job APIs
  2. Scheduled ingestion (Python, GitHub Actions)MY WORK
  3. Clean, deduplicate, normalize, validateMY WORK
  4. Postgres on Supabase → site search and filters
Simplified data flow; highlighted stages show my work.
What it does
Helps students and early-career candidates explore government careers.
What I built
I co-founded PublicPath and was one of two engineers. My work focused on API ingestion and data quality: cleaning, deduplicating, normalizing, and validating listings for search and filtering.
Project artifact
The team homepage shown above, as captured on 6 October 2026.

ToolsPythonGitHub ActionsSupabasePostgres

03Research Assistant, HOPE Lab, UChicago Booth

CLARITY

Turning clinical notes into patient-friendly explanations.

What it does
Part of CLARITY, a healthcare-AI research project: turning de-identified clinical notes into patient-friendly explanatory videos.
What I built
The Python pipeline that automates the explanatory content, narration with Microsoft Neural TTS, slide generation, and MP4 production.
Output
MP4 explanatory videos.Clinical material is not public, so no example is shown.

ToolsPythonMicrosoft Neural TTS

Explanatory illustration · abstract symbols, not real output

  1. 01 · INPUTDe-identified clinical note (JSON)
  2. 02 · MY PIPELINEExplanatory content
  3. 03 · MY PIPELINENarration (Neural TTS) and slides
  4. 04 · MY PIPELINEMP4 video
Simplified sequence automated by my Python pipeline.

Supporting work

  • Energy Market Intelligence

    Ourea Energy · separate from Ourea Brain

    Built a weekly AI-assisted workflow that filtered 50+ recurring signals across energy storage, utilities, and data centers into leadership updates.

  • Chicago Eviction Risk

    Applied ML · private repository

    Built an interpretable machine-learning and geospatial workflow to explore eviction-risk patterns in Chicago.

  • Political Narrative Navigator

    Team project, University of Chicago

    Analyzed 2,000+ presidential campaign speeches alongside YouGov data using AI-assisted text workflows.
    Team repository on GitHub ↗

Background

I combine software building with applied research in machine learning, survey analysis, and geospatial work.

  • Ourea Energy — Agentic AI Intern 2026–
  • Financial Health Network — Research Intern, Data Analytics 2026–
  • HOPE Lab, UChicago Booth — Research Assistant 2026–
  • PublicPath — Co-founder, Engineering and Data 2026–
  • PAMS — Research Associate, ML 2025
  • CERP — Analyst 2024–25
  • MS, Computational Analysis and Public Policy, UChicago expected June 2027
  • BS, Economics and Political Science, Lahore School of Economics May 2024